Overview

Dataset statistics

Number of variables15
Number of observations10626
Missing cells10626
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory120.0 B

Variable types

Numeric14
Unsupported1

Alerts

time(s) is highly correlated with T1 and 12 other fieldsHigh correlation
T1 is highly correlated with time(s) and 12 other fieldsHigh correlation
T2 is highly correlated with time(s) and 12 other fieldsHigh correlation
T3 is highly correlated with time(s) and 12 other fieldsHigh correlation
T4 is highly correlated with time(s) and 12 other fieldsHigh correlation
T5 is highly correlated with time(s) and 12 other fieldsHigh correlation
T6 is highly correlated with time(s) and 12 other fieldsHigh correlation
T7 is highly correlated with time(s) and 12 other fieldsHigh correlation
T8 is highly correlated with time(s) and 12 other fieldsHigh correlation
T9 is highly correlated with time(s) and 12 other fieldsHigh correlation
T10 is highly correlated with time(s) and 12 other fieldsHigh correlation
T11 is highly correlated with time(s) and 12 other fieldsHigh correlation
T12 is highly correlated with time(s) and 12 other fieldsHigh correlation
Z is highly correlated with time(s) and 12 other fieldsHigh correlation
S has 10626 (100.0%) missing values Missing
time(s) is uniformly distributed Uniform
time(s) has unique values Unique
S is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-11-11 03:26:14.679003
Analysis finished2022-11-11 03:26:25.408490
Duration10.73 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

time(s)
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10626
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26562.5
Minimum0
Maximum53125
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:25.435400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2656.25
Q113281.25
median26562.5
Q339843.75
95-th percentile50468.75
Maximum53125
Range53125
Interquartile range (IQR)26562.5

Descriptive statistics

Standard deviation15338.03157
Coefficient of variation (CV)0.5774317768
Kurtosis-1.2
Mean26562.5
Median Absolute Deviation (MAD)13282.5
Skewness0
Sum282253125
Variance235255212.5
MonotonicityStrictly increasing
2022-11-11T11:26:25.495021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
353851
 
< 0.1%
353951
 
< 0.1%
354001
 
< 0.1%
354051
 
< 0.1%
354101
 
< 0.1%
354151
 
< 0.1%
354201
 
< 0.1%
354251
 
< 0.1%
354301
 
< 0.1%
Other values (10616)10616
99.9%
ValueCountFrequency (%)
01
< 0.1%
51
< 0.1%
101
< 0.1%
151
< 0.1%
201
< 0.1%
251
< 0.1%
301
< 0.1%
351
< 0.1%
401
< 0.1%
451
< 0.1%
ValueCountFrequency (%)
531251
< 0.1%
531201
< 0.1%
531151
< 0.1%
531101
< 0.1%
531051
< 0.1%
531001
< 0.1%
530951
< 0.1%
530901
< 0.1%
530851
< 0.1%
530801
< 0.1%

S
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10626
Missing (%)100.0%
Memory size83.1 KiB

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct103
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.68203463
Minimum23.3
Maximum28.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:25.555798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.3
5-th percentile23.3
Q123.7
median27.1
Q327.4
95-th percentile27.7
Maximum28.9
Range5.6
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation1.853461256
Coefficient of variation (CV)0.07216956454
Kurtosis-1.88007678
Mean25.68203463
Median Absolute Deviation (MAD)0.6
Skewness-0.192270453
Sum272897.3
Variance3.435318627
MonotonicityNot monotonic
2022-11-11T11:26:25.611065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.21844
17.4%
27.4979
 
9.2%
23.8808
 
7.6%
23.5770
 
7.2%
27.6760
 
7.2%
23.6694
 
6.5%
27.1647
 
6.1%
23.3624
 
5.9%
23.4527
 
5.0%
23.7451
 
4.2%
Other values (93)2522
23.7%
ValueCountFrequency (%)
23.3624
5.9%
23.352
 
< 0.1%
23.4527
5.0%
23.453
 
< 0.1%
23.5770
7.2%
23.554
 
< 0.1%
23.6694
6.5%
23.654
 
< 0.1%
23.7451
4.2%
23.756
 
0.1%
ValueCountFrequency (%)
28.912
0.1%
28.851
 
< 0.1%
28.751
 
< 0.1%
28.71
 
< 0.1%
28.51
 
< 0.1%
28.451
 
< 0.1%
28.251
 
< 0.1%
28.151
 
< 0.1%
28.0513
0.1%
283
 
< 0.1%

T2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.30031056
Minimum22.6
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:25.660097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.6
5-th percentile22.8
Q123
median23.3
Q323.5
95-th percentile23.8
Maximum23.8
Range1.2
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3054930788
Coefficient of variation (CV)0.01311111618
Kurtosis-0.8992138609
Mean23.30031056
Median Absolute Deviation (MAD)0.2
Skewness-0.1643788012
Sum247589.1
Variance0.09332602119
MonotonicityNot monotonic
2022-11-11T11:26:25.703882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
23.51760
16.6%
23.31578
14.9%
23.41011
9.5%
22.91000
9.4%
23984
9.3%
23.8871
8.2%
23.1746
7.0%
23.7734
6.9%
23.2605
 
5.7%
23.6541
 
5.1%
Other values (3)796
7.5%
ValueCountFrequency (%)
22.681
 
0.8%
22.7221
 
2.1%
22.8494
 
4.6%
22.91000
9.4%
23984
9.3%
23.1746
7.0%
23.2605
 
5.7%
23.31578
14.9%
23.41011
9.5%
23.51760
16.6%
ValueCountFrequency (%)
23.8871
8.2%
23.7734
6.9%
23.6541
 
5.1%
23.51760
16.6%
23.41011
9.5%
23.31578
14.9%
23.2605
 
5.7%
23.1746
7.0%
23984
9.3%
22.91000
9.4%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.18238283
Minimum22.4
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:25.748892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.4
5-th percentile22.6
Q122.8
median23.2
Q323.6
95-th percentile23.8
Maximum23.8
Range1.4
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.4189355774
Coefficient of variation (CV)0.01807129062
Kurtosis-1.312863371
Mean23.18238283
Median Absolute Deviation (MAD)0.4
Skewness-0.04841374595
Sum246336
Variance0.175507018
MonotonicityNot monotonic
2022-11-11T11:26:25.790713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
23.41402
13.2%
23.61402
13.2%
23.81401
13.2%
22.71320
12.4%
22.6827
7.8%
23.3718
6.8%
22.8657
6.2%
23616
5.8%
23.1592
5.6%
22.9552
 
5.2%
Other values (5)1139
10.7%
ValueCountFrequency (%)
22.4121
 
1.1%
22.5277
 
2.6%
22.6827
7.8%
22.71320
12.4%
22.8657
6.2%
22.9552
5.2%
23616
5.8%
23.1592
5.6%
23.2450
 
4.2%
23.3718
6.8%
ValueCountFrequency (%)
23.81401
13.2%
23.7148
 
1.4%
23.61402
13.2%
23.5143
 
1.3%
23.41402
13.2%
23.3718
6.8%
23.2450
 
4.2%
23.1592
5.6%
23616
5.8%
22.9552
 
5.2%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.00089403
Minimum23.3
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:25.836182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.3
5-th percentile23.4
Q123.7
median24
Q324.3
95-th percentile24.7
Maximum24.9
Range1.6
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.3975905676
Coefficient of variation (CV)0.01656565656
Kurtosis-0.7015185816
Mean24.00089403
Median Absolute Deviation (MAD)0.3
Skewness0.1509842953
Sum255033.5
Variance0.1580782595
MonotonicityNot monotonic
2022-11-11T11:26:25.878081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
24.21130
10.6%
24.3957
 
9.0%
23.8933
 
8.8%
24.1874
 
8.2%
23.7828
 
7.8%
23.9817
 
7.7%
24787
 
7.4%
23.6750
 
7.1%
23.5622
 
5.9%
23.3501
 
4.7%
Other values (7)2427
22.8%
ValueCountFrequency (%)
23.3501
4.7%
23.4456
4.3%
23.5622
5.9%
23.6750
7.1%
23.7828
7.8%
23.8933
8.8%
23.9817
7.7%
24787
7.4%
24.1874
8.2%
24.21130
10.6%
ValueCountFrequency (%)
24.9170
 
1.6%
24.8251
 
2.4%
24.7190
 
1.8%
24.6407
 
3.8%
24.5488
4.6%
24.4465
4.4%
24.3957
9.0%
24.21130
10.6%
24.1874
8.2%
24787
7.4%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.69240542
Minimum23.4
Maximum24.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:25.922932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.4
5-th percentile23.4
Q123.5
median23.7
Q323.9
95-th percentile24.1
Maximum24.3
Range0.9
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2298965737
Coefficient of variation (CV)0.009703386785
Kurtosis-0.4061090783
Mean23.69240542
Median Absolute Deviation (MAD)0.2
Skewness0.5709981935
Sum251755.5
Variance0.05285243458
MonotonicityNot monotonic
2022-11-11T11:26:26.026785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
23.62018
19.0%
23.41954
18.4%
23.71696
16.0%
241267
11.9%
23.51246
11.7%
23.8958
9.0%
23.9889
8.4%
24.2216
 
2.0%
24.3210
 
2.0%
24.1172
 
1.6%
ValueCountFrequency (%)
23.41954
18.4%
23.51246
11.7%
23.62018
19.0%
23.71696
16.0%
23.8958
9.0%
23.9889
8.4%
241267
11.9%
24.1172
 
1.6%
24.2216
 
2.0%
24.3210
 
2.0%
ValueCountFrequency (%)
24.3210
 
2.0%
24.2216
 
2.0%
24.1172
 
1.6%
241267
11.9%
23.9889
8.4%
23.8958
9.0%
23.71696
16.0%
23.62018
19.0%
23.51246
11.7%
23.41954
18.4%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.49032562
Minimum23.3
Maximum23.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:26.066692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.3
5-th percentile23.3
Q123.3
median23.5
Q323.6
95-th percentile23.8
Maximum23.9
Range0.6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.1671365693
Coefficient of variation (CV)0.007115123562
Kurtosis-0.9323462413
Mean23.49032562
Median Absolute Deviation (MAD)0.2
Skewness0.4621150992
Sum249608.2
Variance0.02793463279
MonotonicityNot monotonic
2022-11-11T11:26:26.104573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
23.32998
28.2%
23.42022
19.0%
23.52002
18.8%
23.71868
17.6%
23.61058
 
10.0%
23.8516
 
4.9%
23.9162
 
1.5%
ValueCountFrequency (%)
23.32998
28.2%
23.42022
19.0%
23.52002
18.8%
23.61058
 
10.0%
23.71868
17.6%
23.8516
 
4.9%
23.9162
 
1.5%
ValueCountFrequency (%)
23.9162
 
1.5%
23.8516
 
4.9%
23.71868
17.6%
23.61058
 
10.0%
23.52002
18.8%
23.42022
19.0%
23.32998
28.2%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.05725579
Minimum22.3
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:26.147152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.3
5-th percentile22.4
Q122.6
median23.1
Q323.5
95-th percentile23.7
Maximum23.7
Range1.4
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.4390798484
Coefficient of variation (CV)0.01904302283
Kurtosis-1.372399823
Mean23.05725579
Median Absolute Deviation (MAD)0.4
Skewness-0.01694055393
Sum245006.4
Variance0.1927911133
MonotonicityNot monotonic
2022-11-11T11:26:26.187774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
23.31437
13.5%
23.71428
13.4%
23.51351
12.7%
22.51069
10.1%
22.6889
8.4%
22.9717
6.7%
22.4717
6.7%
22.7685
6.4%
22.8560
 
5.3%
23.2550
 
5.2%
Other values (5)1223
11.5%
ValueCountFrequency (%)
22.3110
 
1.0%
22.4717
6.7%
22.51069
10.1%
22.6889
8.4%
22.7685
6.4%
22.8560
5.3%
22.9717
6.7%
23512
4.8%
23.1315
 
3.0%
23.2550
5.2%
ValueCountFrequency (%)
23.71428
13.4%
23.6164
 
1.5%
23.51351
12.7%
23.4122
 
1.1%
23.31437
13.5%
23.2550
 
5.2%
23.1315
 
3.0%
23512
 
4.8%
22.9717
6.7%
22.8560
 
5.3%

T8
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.36667608
Minimum22.9
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:26.232453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.9
5-th percentile23
Q123.2
median23.3
Q323.5
95-th percentile23.7
Maximum23.8
Range0.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2379223796
Coefficient of variation (CV)0.01018212342
Kurtosis-1.104462628
Mean23.36667608
Median Absolute Deviation (MAD)0.2
Skewness-0.0557047304
Sum248294.3
Variance0.05660705873
MonotonicityNot monotonic
2022-11-11T11:26:26.272515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
23.52050
19.3%
23.31962
18.5%
23.71827
17.2%
23.11151
10.8%
231124
10.6%
23.21000
9.4%
23.4665
 
6.3%
23.6541
 
5.1%
22.9163
 
1.5%
23.8143
 
1.3%
ValueCountFrequency (%)
22.9163
 
1.5%
231124
10.6%
23.11151
10.8%
23.21000
9.4%
23.31962
18.5%
23.4665
 
6.3%
23.52050
19.3%
23.6541
 
5.1%
23.71827
17.2%
23.8143
 
1.3%
ValueCountFrequency (%)
23.8143
 
1.3%
23.71827
17.2%
23.6541
 
5.1%
23.52050
19.3%
23.4665
 
6.3%
23.31962
18.5%
23.21000
9.4%
23.11151
10.8%
231124
10.6%
22.9163
 
1.5%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.5630296
Minimum25.325
Maximum29.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:26.323672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.325
5-th percentile26.37
Q126.655
median27.605
Q328.365
95-th percentile28.8875
Maximum29.22
Range3.895
Interquartile range (IQR)1.71

Descriptive statistics

Standard deviation0.9050146142
Coefficient of variation (CV)0.03283436645
Kurtosis-1.354572545
Mean27.5630296
Median Absolute Deviation (MAD)0.855
Skewness-0.002926015229
Sum292884.7525
Variance0.8190514519
MonotonicityNot monotonic
2022-11-11T11:26:26.380503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.56833
 
7.8%
26.655757
 
7.1%
26.465515
 
4.8%
28.365513
 
4.8%
28.745491
 
4.6%
28.175457
 
4.3%
26.845443
 
4.2%
28.27429
 
4.0%
28.08428
 
4.0%
28.555392
 
3.7%
Other values (73)5368
50.5%
ValueCountFrequency (%)
25.32526
0.2%
25.37253
 
< 0.1%
25.4238
0.4%
25.46751
 
< 0.1%
25.5159
 
0.1%
25.56251
 
< 0.1%
25.615
 
< 0.1%
25.65751
 
< 0.1%
25.7057
 
0.1%
25.75251
 
< 0.1%
ValueCountFrequency (%)
29.2215
 
0.1%
29.17252
 
< 0.1%
29.12596
 
0.9%
29.07758
 
0.1%
29.03129
1.2%
28.982514
 
0.1%
28.935247
2.3%
28.887528
 
0.3%
28.84236
2.2%
28.792537
 
0.3%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.19644786
Minimum22.8
Maximum27.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:26.437739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.8
5-th percentile23.465
Q123.94
median25.27
Q326.315
95-th percentile27.075
Maximum27.36
Range4.56
Interquartile range (IQR)2.375

Descriptive statistics

Standard deviation1.252554823
Coefficient of variation (CV)0.04971156372
Kurtosis-1.473286448
Mean25.19644786
Median Absolute Deviation (MAD)1.14
Skewness-0.02658268371
Sum267737.455
Variance1.568893586
MonotonicityNot monotonic
2022-11-11T11:26:26.496541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
23.56772
 
7.3%
26.41555
 
5.2%
26.315520
 
4.9%
23.655441
 
4.2%
26.505429
 
4.0%
24.035413
 
3.9%
23.465400
 
3.8%
24.225361
 
3.4%
26.03352
 
3.3%
26.125328
 
3.1%
Other values (39)6055
57.0%
ValueCountFrequency (%)
22.861
 
0.6%
22.89522
 
0.2%
22.997
 
0.1%
23.0852
 
< 0.1%
23.183
 
< 0.1%
23.2756
 
0.1%
23.3791
 
0.9%
23.465400
3.8%
23.56772
7.3%
23.655441
4.2%
ValueCountFrequency (%)
27.3647
 
0.4%
27.265118
 
1.1%
27.17200
1.9%
27.075179
1.7%
26.98228
2.1%
26.885215
2.0%
26.79185
1.7%
26.695201
1.9%
26.6187
1.8%
26.505429
4.0%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.93158291
Minimum21.7
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:26.547630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile22
Q122.4
median23
Q323.4
95-th percentile23.7
Maximum24
Range2.3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5683131511
Coefficient of variation (CV)0.02478298831
Kurtosis-1.212264942
Mean22.93158291
Median Absolute Deviation (MAD)0.5
Skewness-0.1820944225
Sum243671
Variance0.3229798377
MonotonicityNot monotonic
2022-11-11T11:26:26.595469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23.6854
 
8.0%
23.5850
 
8.0%
23.4807
 
7.6%
22.2658
 
6.2%
22.4607
 
5.7%
23.3605
 
5.7%
22.3598
 
5.6%
23.2568
 
5.3%
22.5554
 
5.2%
22.6524
 
4.9%
Other values (14)4001
37.7%
ValueCountFrequency (%)
21.72
 
< 0.1%
21.8141
 
1.3%
21.9299
2.8%
22219
 
2.1%
22.1249
 
2.3%
22.2658
6.2%
22.3598
5.6%
22.4607
5.7%
22.5554
5.2%
22.6524
4.9%
ValueCountFrequency (%)
2412
 
0.1%
23.984
 
0.8%
23.8317
 
3.0%
23.7520
4.9%
23.6854
8.0%
23.5850
8.0%
23.4807
7.6%
23.3605
5.7%
23.2568
5.3%
23.1431
4.1%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.87885375
Minimum21.7
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:26.643077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile22.2
Q122.4
median22.9
Q323.3
95-th percentile23.7
Maximum23.8
Range2.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.4979202335
Coefficient of variation (CV)0.02176333827
Kurtosis-0.9892342376
Mean22.87885375
Median Absolute Deviation (MAD)0.4
Skewness-0.0467237516
Sum243110.7
Variance0.2479245589
MonotonicityNot monotonic
2022-11-11T11:26:26.684706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
22.3961
 
9.0%
23.4841
 
7.9%
23.3823
 
7.7%
22.6775
 
7.3%
23.1734
 
6.9%
22.2729
 
6.9%
22.4724
 
6.8%
22.7721
 
6.8%
23.2605
 
5.7%
22.9587
 
5.5%
Other values (12)3126
29.4%
ValueCountFrequency (%)
21.7136
 
1.3%
21.848
 
0.5%
21.945
 
0.4%
2223
 
0.2%
22.146
 
0.4%
22.2729
6.9%
22.3961
9.0%
22.4724
6.8%
22.5572
5.4%
22.6775
7.3%
ValueCountFrequency (%)
23.8168
 
1.6%
23.7413
3.9%
23.6468
4.4%
23.5429
4.0%
23.4841
7.9%
23.3823
7.7%
23.2605
5.7%
23.1734
6.9%
23407
3.8%
22.9587
5.5%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct141
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean929.8667307
Minimum900.656
Maximum977.438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2022-11-11T11:26:26.736856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum900.656
5-th percentile901.875
Q1903.094
median909.4925
Q3962.2035
95-th percentile975
Maximum977.438
Range76.782
Interquartile range (IQR)59.1095

Descriptive statistics

Standard deviation29.37278413
Coefficient of variation (CV)0.03158816544
Kurtosis-1.593246873
Mean929.8667307
Median Absolute Deviation (MAD)7.6175
Skewness0.39095126
Sum9880763.88
Variance862.7604474
MonotonicityNot monotonic
2022-11-11T11:26:26.795621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
903.0942926
27.5%
901.875902
 
8.5%
904.313586
 
5.5%
973.781320
 
3.0%
902.4845278
 
2.6%
903.7035275
 
2.6%
976.219258
 
2.4%
975242
 
2.3%
968.906232
 
2.2%
967.688227
 
2.1%
Other values (131)4380
41.2%
ValueCountFrequency (%)
900.65614
 
0.1%
901.265529
 
0.3%
901.875902
 
8.5%
902.4845278
 
2.6%
903.0942926
27.5%
903.7035275
 
2.6%
904.313586
 
5.5%
904.92225
 
0.2%
905.53193
 
0.9%
905.53151
 
< 0.1%
ValueCountFrequency (%)
977.43871
 
0.7%
976.82855
 
< 0.1%
976.219258
2.4%
975.609545
 
0.4%
975242
2.3%
974.390524
 
0.2%
973.78151
 
< 0.1%
973.781320
3.0%
973.17219
 
0.2%
972.563180
1.7%

Interactions

2022-11-11T11:26:24.403024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.132878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.874571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.540262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.293052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.994584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.758934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.418935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.177255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.864380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.589048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.362521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.012679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.744056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.453853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.181714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.922271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.590094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.343180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.045413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.805779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.466918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.227087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.911103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.637883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.408366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.058525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.790977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.504260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.230549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.967084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.637982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.391841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.094302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.851903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.514349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.275002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.022448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.687011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.453215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.104683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.835880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.557519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.281550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.015419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.687764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.442514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.144505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.900679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.563151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.325284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.071318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.737803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.500293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.152997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.883794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.612334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.333375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.065815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.739443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.495814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.196984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.950131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.615050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.377110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.120120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.790878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.549287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.201887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.933626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.665230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.385201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.115644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.790217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.547640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.248904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.998966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.738218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.428017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.169023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.843700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.597098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.252701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.982461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.713968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.432043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.161539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.837387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.595768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.297045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.044667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.786057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.474434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.213838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.891538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.642034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.298546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.028046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.766390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.483873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.210375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.887638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.647655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.411659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.092435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.836102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.524792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.261680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.942367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.689924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.349375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.077833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.818183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.533705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.258213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.937884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.698267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.462541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.141741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.886264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.574907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.310255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.993567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.737779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.399207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.125672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.930831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.580140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.303881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.984886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.746112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.511273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.186638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.933409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.622129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.355104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.041182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.781715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.444056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.169657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.984084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.632962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.355222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.101457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.798993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.563515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.235833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.984638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.672925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.404936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.094424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.830828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.493888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.219456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:25.032013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.678044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.400029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.148304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.847260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.610353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.280833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.031070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.719997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.449785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.141267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.874644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.605511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.263309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:25.081731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.724010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.445875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.194981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.894444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.658648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.325491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.079906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.766158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.494477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.190102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.919492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.650377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.308284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:25.128696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:15.823674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:16.489875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.241227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:17.941755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:18.705489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:19.368843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.124841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:20.812472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:21.538388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.308702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:22.962827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:23.694266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:24.352196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:26:26.854414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:26:26.990953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:26:27.072678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:26:27.154023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:26:27.234845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:26:25.215766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:26:25.324633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-11T11:26:25.372669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

time(s)ST1T2T3T4T5T6T7T8T9T10T11T12Z
00NaN23.423.223.223.323.523.823.223.225.4222.99021.921.8968.297
15NaN23.423.223.223.323.523.823.223.225.4222.99021.921.8967.688
210NaN23.423.223.223.323.523.823.223.225.4222.99021.921.8967.688
315NaN23.423.223.223.323.523.823.223.225.4222.89521.921.8967.688
420NaN23.423.223.223.323.523.823.223.225.4222.89521.921.8967.688
525NaN23.423.223.223.323.523.823.223.225.4222.89521.921.8967.688
630NaN23.423.223.223.323.523.823.223.225.4222.89521.921.8968.297
735NaN23.423.223.223.323.523.823.223.225.4222.89521.921.8968.297
840NaN23.423.223.223.323.523.823.223.225.4222.89521.921.8968.297
945NaN23.423.223.223.323.523.823.223.225.4222.89521.921.8968.297

Last rows

time(s)ST1T2T3T4T5T6T7T8T9T10T11T12Z
1061653080NaN27.823.723.424.924.323.923.323.827.98526.12523.623.6903.0940
1061753085NaN27.823.723.424.924.323.923.323.827.98526.12523.623.6903.0940
1061853090NaN27.823.723.424.924.323.923.323.827.98526.12523.623.6902.4845
1061953095NaN27.823.723.424.924.323.923.323.827.98526.03023.623.6902.4845
1062053100NaN27.823.723.424.924.323.923.323.827.98526.03023.623.6902.4845
1062153105NaN27.823.723.424.924.323.923.323.827.98526.03023.623.6903.0940
1062253110NaN27.823.723.424.924.323.923.323.827.98526.03023.623.6903.0940
1062353115NaN27.823.723.424.924.323.923.323.827.98526.03023.623.6903.0940
1062453120NaN27.823.723.424.924.323.923.323.827.98526.03023.623.6902.4845
1062553125NaN27.823.723.424.924.323.923.323.827.98525.93523.623.6901.8750